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跨资产动量溢出与基于网络的期货交易

文章 arXiv papers · 作者: Xingyue Pu et al.
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总结

这篇论文研究一个期货市场中的动量信息能否帮助预测其他市场的动量。研究根据大宗商品、股票、债券和货币领域中64个连续交易合约的价格动量特征构建网络，无需预先指定公司联系或其他经济关系。

一种线性且可解释的图学习方法估计合约间的联系，所得网络用于构建多资产动量策略。论文报告称，经过波动率缩放后，该策略在2000至2022年间实现了1.5的夏普比率和22%的年化收益。研究将这些发现作为实证依据，支持在组合信号中使用跨市场动量联系。文档仅提供概要绩效数据；没有说明交易成本、实施细节或稳健性检验，因此仅凭该摘要无法评估结果在实际应用中的局限。

核心观点

  • 动量行为相似的资产之间可能会传播动量风险溢价。
  • 仅使用价格特征学习出的网络即可表示跨市场动量联系。
  • 分析覆盖四大资产类别中的64个期货合约。
  • 研究使用线性图学习模型，使推断出的联系保持可解释。
  • 该网络用于构建波动率缩放策略，报告了2000至2022年间的表现。

标签

多资产期货动量机器学习投资组合构建
全文
# Network Momentum across Asset Classes


# Network Momentum across Asset Classes









We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic and fundamental ties, such as the stock-bond connection of the same company and stocks linked through supply-demand chains, momentum spillover implies a propagation of momentum risk premium from one asset to another. The similarity of momentum risk premium, exemplified by co-movement patterns, has been spotted across multiple asset classes including commodities, equities, bonds and currencies. However, studying the network effect of momentum spillover across these classes has been challenging due to a lack of readily available common characteristics or economic ties beyond the company level. In this paper, we explore the interconnections of momentum features across a diverse range of 64 continuous future contracts spanning these four classes. We utilise a linear and interpretable graph learning model with minimal assumptions to reveal the intricacies of the momentum spillover network. By leveraging the learned networks, we construct a network momentum strategy that exhibits a Sharpe ratio of 1.5 and an annual return of 22%, after volatility scaling, from 2000 to 2022. This paper pioneers the examination of momentum spillover across multiple asset classes using only pricing data, presents a multi-asset investment strategy based on network momentum, and underscores the effectiveness of this strategy through robust empirical analysis.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。